Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images
Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cance...
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| Veröffentlicht in: | Computers, materials & continua materials & continua, 2022, Vol.72 (2), p.3799-3813 |
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| container_title | Computers, materials & continua |
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| creator | A. Malibari, Areej Alshahrani, Reem N. Al-Wesabi, Fahd Ben Haj Hassine, Siwar Abdullah Alkhonaini, Mimouna Mustafa Hilal, Anwer |
| description | Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%. |
| doi_str_mv | 10.32604/cmc.2022.026131 |
| format | Article |
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Malibari, Areej ; Alshahrani, Reem ; N. Al-Wesabi, Fahd ; Ben Haj Hassine, Siwar ; Abdullah Alkhonaini, Mimouna ; Mustafa Hilal, Anwer</creator><creatorcontrib>A. Malibari, Areej ; Alshahrani, Reem ; N. Al-Wesabi, Fahd ; Ben Haj Hassine, Siwar ; Abdullah Alkhonaini, Mimouna ; Mustafa Hilal, Anwer</creatorcontrib><description>Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized for classification process where the class labels are allotted to the input MRI images. Moreover, the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm (KHA) which results in improved classification performance. In order to demonstrate the good classification outcome of the DTL-PSCC technique, a wide range of simulations take place on benchmark MRI datasets. 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| subjects | Algorithms Artificial intelligence Classification Decision making Feature extraction Image classification Image processing Krill Machine learning Magnetic resonance imaging Medical imaging Medical research Prostate cancer |
| title | Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images |
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